stanfordmlgroup/ngboost

Health v2 · maintenance only

Adoption not part of the score

1887 stars · 253 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NGBoost is a Python library implementing Natural Gradient Boosting for probabilistic prediction, built on top of scikit-learn. It outputs full predictive distributions rather than point estimates, with modular choice of base learner, distribution, and scoring rule.

Use cases

• predict a full probability distribution instead of a point estimate with gradient boosting
• estimate uncertainty in regression predictions
• fit a gradient boosting model that outputs mean and variance
• probabilistic regression on tabular data in Python
• compare boosting models using negative log likelihood
• get calibrated prediction intervals from tree ensembles

When to choose

• you need uncertainty quantification from gradient boosting on tabular data
• you want a scikit-learn-compatible probabilistic regressor
• you need to model output distributions like Normal or Poisson with boosting

When to avoid

• you only need fast point predictions where XGBoost or LightGBM are more optimized
• you need classification with modern GPU-accelerated boosting
• you require very large-scale training with heavy ecosystem support

Facets

library · maturity active

machine-learning data-science machine-learning data-science python gradient-boosting uncertainty-estimation probabilistic-prediction natural-gradients scikit-learn regression

Member repositories

For agents

markdown · JSON · MCP: product_card(name="stanfordmlgroup/ngboost")